lava-dl
Andrew-NG-Notes
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| 1 | 1 | |
| 140 | 2,315 | |
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| 7.8 | 0.0 | |
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| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | - |
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lava-dl
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Has anyone used Spiking Neural Networks (SNNs) for image processing?
Surrogate gradient learning w/ backpropagation: for short, you can use backpropagation with SNNs (by a little trick during the backward pass). Super easy to implement, super efficient. You have a deep SNN trained via backprop with any type of input you want. Personally, that is completely my jam. Maybe you can use such paradigm to easily train an SNN in your biomed image dataset. Good repos: SnnTorch comes with the best tutorials to explain SNNs and surrogate gradient learning. This is the fastest way to understand the field and begin to implement you solution. Nevertheless, spikingjelly remains a better option when it comes to implement your ideas (better memory efficiency, etc). Good mention to lava-dl, with which you can train a neural network and directly transfer it into neuromorphic hardware (Intel Loihi) if you have access to this kind of chip.
Andrew-NG-Notes
What are some alternatives?
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
rtdl-revisiting-models - (NeurIPS 2021) Revisiting Deep Learning Models for Tabular Data
machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
learnopencv - Learn OpenCV : C++ and Python Examples
gdrl - Grokking Deep Reinforcement Learning
shap - A game theoretic approach to explain the output of any machine learning model.
fsdl-text-recognizer-2022-labs - Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
DeepNeuralNetworksFromScratch - Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.
strategy-ml-nn - This example shows how to use neural networks for writing a trading system on stocks.
embedml - pytorch like machine learning framework from scratch
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, BEiT, Swin Transformer, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.